Modeling DNA Diversity in Reverse Cholesterol Transport
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To identify genetic variation in reverse cholesterol transport (RCT) and its role in cardiovascular disease and atherosclerosis.
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BACKGROUND:
The collaborative research project will address a major problem in genetic epidemiology, namely the development of strategies to rationally identify and select sites or combinations of sites for genotype-phenotype studies that will be most relevant for predicting and understanding the role of genes in physiological phenotypes. Knowledge could be advanced in both specific and general terms. The project will focus on the genetic architecture of 62 genes either known or hypothesized to have a role in cholesterol transport. The data and conclusions generated by this project will enrich our understanding of both the extant genetic variation in these genes and the functional or causative relationships between such variation and phenotypes measured at a variety of organizational scales (pathway, systemic and clinical outcome). The additional focus on environmental interaction terms will enhance understanding of specific gene x environment interactions in the cholesterol transport system. The project involves three collaborating grants: R01HL72810 to Eric A. Boerwinkle at the University of Texas School of Public Health; R01HL72905 to Charles F. Sing at the University of Michigan at Ann Arbor; and R01HL72904 to Andrew Clark at Cornell University in Ithaca, New York.
DESIGN NARRATIVE:
This Collaborative Research Project will model DNA diversity in Reverse Cholesterol Transport (RCT) by exploring the genetic basis underlying this pathway and its contribution to atherosclerosis risk and cardiovascular disease (CVD). Given the progress of the Human Genome Project and the development of laboratory and analytic resources, the delineation of a gene's complete genetic architecture and contribution to individual differences within a population becomes (theoretically) feasible. This study will explore the variation in 8 of 62 candidate genes that will be screened using a battery of single nucleotide polymorphisms (SNPs). The eight genes (and the 6-10 SNPs within each) will be chosen based upon association with a surrogate quantitative trait (using a cholesterol efflux assay). The genes will be exhaustively characterized by analysis of DNA sequence variation, linkage disequilibrium (LD), and haplotypes in samples from the community-based CARDIA study. Genotype-phenotype relationships will be determined using analytic methods that take advantage of population/evolutionary history, underlying complexity, and other innovative approaches.
Component 1 of this Collaborative Research Project under Eric Boerwinkle will generate a large amount of new data on complex patterns of variation in a substantial number of genes. A major objective is to develop methods for analyzing these data to find etiologically relevant aspects of variation. For each of 62 candidate genes, the initial responsibility of Component 2 under Andrew Clark is to analyze the distribution of single DNA site and haplotype variation in terms of the population processes responsible for that variation. The investigators will develop systematic methods to identify subsets of sites that most fully capture the haplotypic structure of the data and thereby reduce the dimensionality of the variation. These methods will be applied to haplotype and genotype variation in a population-based sample of 2007 African-Americans and 2139 European-Americans from the CARDIA study. Single nucleotide polymorphisms frequency spectra, linkage disequilibrium and the block-wise structure of haplotypes will be quantified and related to expectations of population genetic theory. Novel approaches to genotype-phenotype associations will be pursued, in close collaboration with Component 3, by testing the fit of the neutral site frequency spectrum to data stratified by phenotypic measures.
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